PROJECT SUMMARY The current proposal seeks to clarify the mechanisms underlying suicide death. Suicide constitutes a severe and steadily worsening public health crisis, and suicide prevention has become a primary focus of NIMH efforts. Aggregated data across multiple large genetic studies yield heritability estimates of suicide death at approximately 45%. However, research on risk factors to date has been largely confined to epidemiological observations, with a lack of access to molecular genetic data on suicide death. This lack of access has resulted in an overwhelming focus on the genetic study of subthreshold phenotypes?ideation and attempt? which very rarely result in suicide. Currently, positive predictive values for suicide attempt are high (.9), while positive predictive values for suicide death continue to hover near zero. This research team has unprecedented access to DNA from thousands of independent, population-based suicide deaths from the Utah Office of the Medical Examiner. DNA resources are enhanced by a wealth of electronic medical record and environmental exposure data on all suicides, using the Utah Population Database, a unique resource of >10 million residents. Due to the extreme and unambiguous nature of suicide relative to psychiatric phenotypes, genotyping and genome-wide association analysis of the first 3,413 cases and 14,848 matched controls has already resulted in genome-wide significant signals and strong polygenic signal. Five novel, rare missense SNPs are also significantly associated with suicide death in these preliminary data. By genotyping additional and incoming suicide deaths, this project aims to replicate and significantly expand on genetic discoveries. In addition, approximately 20% of the population-based suicides evidence significant ancestry admixture, providing valuable diversity to enhance both discovery and generalizability. This research team will work closely in partnership with the Psychiatric Genomics Consortium and UK Biobank to examine new data on suicide death, test clinically informative risk models, and leverage large external cohorts to model complex suicide etiologies. Some of the high-impact deliverables from this project include a) comprehensive co- morbidity, mode of death, and risk factor statistics from the largest population-based suicide cohort to date, b) the first genome-wide data and summary statistics for suicide death, linked to a wealth of risk phenotypes, polygenic risks, and diagnoses (e.g., ADHD, affective disorders, alcohol use disorder, autism spectrum disorder, pain, mania, metabolic conditions, opiate use, pregnancy, psychosis), c) genetic correlation estimates of suicide death with a range of phenotypes, for the development of genetic risk models, and d) clinically informative genetic and environmental predictors of suicide, accounting for sex, ancestry, and age.